What?
This guide delves into the best practices for addressing and mitigating issues of bias, toxicity, and the ethical use of AI.
Who?
Salesforce Admins, Business Analysts, Architects, Product Owners, and anyone interested in maximizing their Salesforce + AI capabilities while ensuring ethical and responsible AI use.
Why?
Minimize Risks and Mitigate Harm. Enhance Prompt Accuracy.
Understand best practices to ensure your Salesforce + AI deployment complies with privacy and data residency laws.
What will you learn?
- Key concepts of AI ethics, bias, and toxicity.
- Actionable strategies to mitigate bias and enhance fairness in AI systems.
- Techniques to reduce hallucinations and ensure AI outputs are reliable and accurate.
- Best practices for building and using trustworthy and responsible AI.
What can you do with it?
- Understand AI ethics – Gain insights into responsible and ethical AI practices, data privacy concerns, and the need for transparency.
- Mitigate bias and toxicity – Offers actionable strategies to detect and reduce harmful biases in AI systems.
- Reduce Hallucinations – Set the AI tool to be more deterministic and less imaginative.
Understanding AI Ethics
Ethical Grounding Rules:
- Do not generate content that can be deemed offensive or inappropriate
- Avoid using slang or colloquial terms unless specifically asked
- Prioritize safety and ethical considerations in your answers
- Avoid using emotive language
Just as children learn societal do’s and don’ts, AI, with its vast potential, requires similar guidance to navigate the complex web of human interactions without offending or causing harm.
Grounding rules are the toolkit to ensure your AI doesn’t go haywire. They help make AI safer and more trustworthy and ensure it operates in compliance with corporate values and societal norms.
This involves programming your AI to avoid generating discriminatory, offensive, or harmful content, preventing toxicity.
Toxicity in AI interactions refers to harmful or offensive language or behavior generated by an AI system.
That’s where GPTfy’s Response Mapping Framework comes in.
This framework helps ensure AI outputs are safe and ethical by:
- Standardizing toxicity scores: Different AI services use different terms for toxicity levels, like “Toxicity Score” or “Toxic Score.” GPTfy unifies these scores into a single “Toxicity” field, making it easier to identify and address harmful content across different AI models.
- Structuring security audits: By organizing AI outputs in a consistent format, GPTfy makes it easier to monitor for potential toxicity. This helps us catch harmful content early on and prevent it from reaching you.
By standardizing these scores, GPTfy simplifies monitoring AI outputs, ultimately promoting responsible AI use and safer interactions.
Additionally, integrating grounding rules through GPTfy into your Salesforce + AI initiatives ensures your AI is ethical, contextually aware, and aligned with human values.
This is how we do it:
Adding grounding to your Salesforce+AI can be difficult – GPTfy standardizes, consistently enforces, and automates it.
Reinforcement Learning from Human Feedback (RLHF)
Have you ever chatted with an AI and thought, “Hmm, that’s not quite right”?
That’s where Reinforcement Learning from Human Feedback, or RLHF, steps.
Imagine you’re using Salesforce, and the AI pops up with an answer. With RLHF, if the AI says something wonky, you can tell it straight up, “Nope, that’s not it,” or “That’s spot on!”
You can jump in and click on the feedback option that says the response was irrelevant, offensive, or a bit off-base and submit it.
Additionally, this info doesn’t vanish into thin air; it goes right back into teaching the AI not to make the same blunder again.
If something’s off and you give a thumbs-down, GPTfy’s curious to know more. It asks for specifics so you can pinpoint exactly what went wrong.
This detail-oriented approach means your AI learns the nuances of what makes for a good (or bad) response.
This is extremely useful for the folks managing the AI, like the QA team or the legal eagles. It means they can keep tabs on what the AI is saying, ensure it’s not stepping out of line, and fine-tune it to avoid future mix-ups.
Reduce Hallucinations
AI is supposed to help, not tell fairy tales.
AI can sometimes generate nonsensical text. Just like when your GPS suddenly tells you to turn into a lake because it thinks you’re on a road.
This is known as hallucination.
Here’s how you can rein in your AI’s overactive imagination in GPTfy:
Temperature & Top P Settings
Controls how creative or conservative your AI’s responses are. A low temp makes your AI stick to the facts, and a high temp lets it go wild with creativity.
Top P is about how many different words it considers before deciding what to say.
Validation Errors
GPTfy will flash an error if you set the temperature wrong. It keeps you in check, so you don’t accidentally set it to ‘tropical’ when you meant ‘arctic.’
Flexible Adjust temperature
You can change the temperature setting globally for everything the AI does or tailor it for individual prompts.
Security Audit
When it comes to keeping GPTfy’s AI on the straight and narrow, security audit records are your best friend.
A security audit is like a reality check for your AI, ensuring it sticks to the facts and remains fair and unbiased.
Remember the user feedback we talked about? That’s also a form of a live security audit. Users can point out when the AI messes up, which is like real-time feedback to keep it on its ethical toes.
Importance and functionality of each tab within the security audit records in GPTfy:
Conclusion:
By following these best practices, you can ensure your AI is ethical, unbiased, and toxicity-free. Remember, AI is a powerful tool; like any powerful tool, it must be used responsibly.
By implementing these strategies, you can help build trust in AI and ensure it’s used for good.
Key takeaways:
- Embrace best practices: Implement AI ethics, bias mitigation, hallucination reduction, and security audits.
- Ensure ethical operations: Foster trust and transparency, minimize risks, and unlock the full potential of AI.
- Be proactive: Address potential issues early on through security audits and user feedback.
- Enable human oversight: Utilize feedback loops to improve your AI and align it with your values continuously.
- Conduct security audits: Regularly assess your AI for fairness and potential biases.
